{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a90127e",
   "metadata": {},
   "source": [
    "属性  | 定义 \n",
    "------|------\n",
    "datetime | 时间  \n",
    "Global_active_power  | 每分钟的有功功率(千瓦)      \n",
    "Global_reactive_power  | 每分钟的无功功率(千瓦)    \n",
    "Voltage  | 每分钟的平均电压(伏特)      \n",
    "Global_intensity  | 每分钟的平均电流强度(安培)      \n",
    "Sub_metering_1  | 厨房有功电能(瓦时),主要包含洗碗机,烤箱和微波炉 \n",
    "Sub_metering_2  | 用于洗衣房有功电能(瓦时),包含洗衣机,滚筒式烘干机,冰箱和电灯  \n",
    "Sub_metering_3  | 电热水器和空调有功电能(瓦时)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "526224f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.feature_selection import SelectPercentile, f_classif\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, roc_curve, r2_score,mean_squared_error, recall_score\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option('mode.chained_assignment',None)\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e78503",
   "metadata": {},
   "source": [
    "读取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c37fba4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2075259, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Global_active_power</th>\n",
       "      <th>Global_reactive_power</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>Sub_metering_1</th>\n",
       "      <th>Sub_metering_2</th>\n",
       "      <th>Sub_metering_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-12-16 17:24:00</th>\n",
       "      <td>4.216</td>\n",
       "      <td>0.418</td>\n",
       "      <td>234.840</td>\n",
       "      <td>18.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-16 17:25:00</th>\n",
       "      <td>5.360</td>\n",
       "      <td>0.436</td>\n",
       "      <td>233.630</td>\n",
       "      <td>23.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-16 17:26:00</th>\n",
       "      <td>5.374</td>\n",
       "      <td>0.498</td>\n",
       "      <td>233.290</td>\n",
       "      <td>23.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-16 17:27:00</th>\n",
       "      <td>5.388</td>\n",
       "      <td>0.502</td>\n",
       "      <td>233.740</td>\n",
       "      <td>23.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-16 17:28:00</th>\n",
       "      <td>3.666</td>\n",
       "      <td>0.528</td>\n",
       "      <td>235.680</td>\n",
       "      <td>15.800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Global_active_power Global_reactive_power  Voltage  \\\n",
       "datetime                                                                 \n",
       "2006-12-16 17:24:00               4.216                 0.418  234.840   \n",
       "2006-12-16 17:25:00               5.360                 0.436  233.630   \n",
       "2006-12-16 17:26:00               5.374                 0.498  233.290   \n",
       "2006-12-16 17:27:00               5.388                 0.502  233.740   \n",
       "2006-12-16 17:28:00               3.666                 0.528  235.680   \n",
       "\n",
       "                    Global_intensity Sub_metering_1 Sub_metering_2  \\\n",
       "datetime                                                             \n",
       "2006-12-16 17:24:00           18.400          0.000          1.000   \n",
       "2006-12-16 17:25:00           23.000          0.000          1.000   \n",
       "2006-12-16 17:26:00           23.000          0.000          2.000   \n",
       "2006-12-16 17:27:00           23.000          0.000          1.000   \n",
       "2006-12-16 17:28:00           15.800          0.000          1.000   \n",
       "\n",
       "                     Sub_metering_3  \n",
       "datetime                             \n",
       "2006-12-16 17:24:00            17.0  \n",
       "2006-12-16 17:25:00            16.0  \n",
       "2006-12-16 17:26:00            17.0  \n",
       "2006-12-16 17:27:00            17.0  \n",
       "2006-12-16 17:28:00            17.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('household_power_consumption.txt',sep=';',\n",
    "                  header=0,low_memory=False,infer_datetime_format=True,\n",
    "                  parse_dates={'datetime':[0,1]}, index_col=['datetime'])\n",
    "\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf03f39",
   "metadata": {},
   "source": [
    "### 探索性分析\n",
    "\n",
    "查看缺失值情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ffa450f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Global_active_power</th>\n",
       "      <th>Global_reactive_power</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>Sub_metering_1</th>\n",
       "      <th>Sub_metering_2</th>\n",
       "      <th>Sub_metering_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2075259</td>\n",
       "      <td>2075259</td>\n",
       "      <td>2075259</td>\n",
       "      <td>2075259</td>\n",
       "      <td>2075259</td>\n",
       "      <td>2075259</td>\n",
       "      <td>2.049280e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4187</td>\n",
       "      <td>533</td>\n",
       "      <td>2838</td>\n",
       "      <td>222</td>\n",
       "      <td>89</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>?</td>\n",
       "      <td>0.000</td>\n",
       "      <td>?</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>25979</td>\n",
       "      <td>481561</td>\n",
       "      <td>25979</td>\n",
       "      <td>172785</td>\n",
       "      <td>1880175</td>\n",
       "      <td>1436830</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.458447e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.437154e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.700000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.100000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Global_active_power Global_reactive_power  Voltage Global_intensity  \\\n",
       "count              2075259               2075259  2075259          2075259   \n",
       "unique                4187                   533     2838              222   \n",
       "top                      ?                 0.000        ?            1.000   \n",
       "freq                 25979                481561    25979           172785   \n",
       "mean                   NaN                   NaN      NaN              NaN   \n",
       "std                    NaN                   NaN      NaN              NaN   \n",
       "min                    NaN                   NaN      NaN              NaN   \n",
       "25%                    NaN                   NaN      NaN              NaN   \n",
       "50%                    NaN                   NaN      NaN              NaN   \n",
       "75%                    NaN                   NaN      NaN              NaN   \n",
       "max                    NaN                   NaN      NaN              NaN   \n",
       "\n",
       "       Sub_metering_1 Sub_metering_2  Sub_metering_3  \n",
       "count         2075259        2075259    2.049280e+06  \n",
       "unique             89             82             NaN  \n",
       "top             0.000          0.000             NaN  \n",
       "freq          1880175        1436830             NaN  \n",
       "mean              NaN            NaN    6.458447e+00  \n",
       "std               NaN            NaN    8.437154e+00  \n",
       "min               NaN            NaN    0.000000e+00  \n",
       "25%               NaN            NaN    0.000000e+00  \n",
       "50%               NaN            NaN    1.000000e+00  \n",
       "75%               NaN            NaN    1.700000e+01  \n",
       "max               NaN            NaN    3.100000e+01  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63cfc83d",
   "metadata": {},
   "source": [
    "有默认标记的异常值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "55bf15e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace('?',np.nan,inplace=True)\n",
    "data = data.astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca06b540",
   "metadata": {},
   "source": [
    "查看缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1bbbc347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power      25979\n",
       "Global_reactive_power    25979\n",
       "Voltage                  25979\n",
       "Global_intensity         25979\n",
       "Sub_metering_1           25979\n",
       "Sub_metering_2           25979\n",
       "Sub_metering_3           25979\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8089480",
   "metadata": {},
   "source": [
    "使用前一天的同一时间复制值进行缺失值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b18e832d",
   "metadata": {},
   "outputs": [],
   "source": [
    "values = data.values\n",
    "one_day = 60 * 24\n",
    "for row in range(values.shape[0]):\n",
    "    for col in range(values.shape[1]):\n",
    "        if np.isnan(values[row, col]):\n",
    "            values[row, col] = values[row - one_day, col]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c99bafa2",
   "metadata": {},
   "source": [
    "再查看缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "52385bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Global_active_power      0\n",
       "Global_reactive_power    0\n",
       "Voltage                  0\n",
       "Global_intensity         0\n",
       "Sub_metering_1           0\n",
       "Sub_metering_2           0\n",
       "Sub_metering_3           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5248fdc",
   "metadata": {},
   "source": [
    "添加剩余用电量列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "af488137",
   "metadata": {},
   "outputs": [],
   "source": [
    "values = data.values\n",
    "data['sub_metering_4'] = (values[:,0] * 1000 / 60) - (values[:,4] + values[:,5] + values[:,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "26488520",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('household_power_consumption.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec06830",
   "metadata": {},
   "source": [
    "计算每日总功耗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "08ad5a12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1442, 8)\n",
      "            Global_active_power  Global_reactive_power    Voltage  \\\n",
      "datetime                                                            \n",
      "2006-12-16             1209.176                 34.922   93552.53   \n",
      "2006-12-17             3390.460                226.006  345725.32   \n",
      "2006-12-18             2203.826                161.792  347373.64   \n",
      "2006-12-19             1666.194                150.942  348479.01   \n",
      "2006-12-20             2225.748                160.998  348923.61   \n",
      "\n",
      "            Global_intensity  Sub_metering_1  Sub_metering_2  Sub_metering_3  \\\n",
      "datetime                                                                       \n",
      "2006-12-16            5180.8             0.0           546.0          4926.0   \n",
      "2006-12-17           14398.6          2033.0          4187.0         13341.0   \n",
      "2006-12-18            9247.2          1063.0          2621.0         14018.0   \n",
      "2006-12-19            7094.0           839.0          7602.0          6197.0   \n",
      "2006-12-20            9313.0             0.0          2648.0         14063.0   \n",
      "\n",
      "            sub_metering_4  \n",
      "datetime                    \n",
      "2006-12-16    14680.933319  \n",
      "2006-12-17    36946.666732  \n",
      "2006-12-18    19028.433281  \n",
      "2006-12-19    13131.900043  \n",
      "2006-12-20    20384.800011  \n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv('household_power_consumption.csv', header=0, infer_datetime_format=True, parse_dates=['datetime'], index_col=['datetime'])\n",
    "daily_groups = dataset.resample('D')\n",
    "daily_data = daily_groups.sum()\n",
    "print(daily_data.shape)\n",
    "print(daily_data.head())\n",
    "daily_data.to_csv('household_power_consumption_days.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "065cbe14",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import sklearn.metrics as skm\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "from tensorflow.keras.layers import RepeatVector,TimeDistributed\n",
    "#from torch import nn\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "def split_dataset(data):\n",
    "    '''\n",
    "    该函数实现以周为单位切分训练数据和测试数据\n",
    "    '''\n",
    "    # data为按天的耗电量统计数据，shape为(1442, 8)\n",
    "    # 测试集取最后一年的46周（322天）数据，剩下的159周（1113天）数据为训练集，以下的切片实现此功能。\n",
    "    train, test = data[1:-328], data[-328:-6]\n",
    "    train = np.array(np.split(train, len(train) / 7))  # 将数据划分为按周为单位的数据\n",
    "    test = np.array(np.split(test, len(test) / 7))\n",
    "    return train, test\n",
    "\n",
    "\n",
    "def evaluate_forecasts(actual, predicted):\n",
    "    '''\n",
    "    该函数实现根据预期值评估一个或多个周预测损失\n",
    "    思路：统计所有单日预测的 RMSE\n",
    "    '''\n",
    "    scores = list()\n",
    "    for i in range(actual.shape[1]):\n",
    "        mse = skm.mean_squared_error(actual[:, i], predicted[:, i])\n",
    "        rmse = math.sqrt(mse)\n",
    "        scores.append(rmse)\n",
    "\n",
    "    s = 0  # 计算总的 RMSE\n",
    "    for row in range(actual.shape[0]):\n",
    "        for col in range(actual.shape[1]):\n",
    "            s += (actual[row, col] - predicted[row, col]) ** 2\n",
    "    score = math.sqrt(s / (actual.shape[0] * actual.shape[1]))\n",
    "    print('actual.shape[0]:{}, actual.shape[1]:{}'.format(actual.shape[0], actual.shape[1]))\n",
    "    return score, scores\n",
    "\n",
    "\n",
    "def summarize_scores(name, score, scores):\n",
    "    s_scores = ', '.join(['%.1f' % s for s in scores])\n",
    "    print('%s: [%.3f] %s\\n' % (name, score, s_scores))\n",
    "\n",
    "\n",
    "def sliding_window(train, sw_width=7, n_out=7, in_start=0):\n",
    "    '''\n",
    "    该函数实现窗口宽度为7、滑动步长为1的滑动窗口截取序列数据\n",
    "    '''\n",
    "    data = train.reshape((train.shape[0] * train.shape[1], train.shape[2]))  # 将以周为单位的样本展平为以天为单位的序列\n",
    "    X, y = [], []\n",
    "\n",
    "    for _ in range(len(data)):\n",
    "        in_end = in_start + sw_width\n",
    "        out_end = in_end + n_out\n",
    "\n",
    "        # 保证截取样本完整，最大元素索引不超过原序列索引，则截取数据；否则丢弃该样本\n",
    "        if out_end < len(data):\n",
    "            # 训练数据以滑动步长1截取\n",
    "            train_seq = data[in_start:in_end, 0]\n",
    "            train_seq = train_seq.reshape((len(train_seq), 1))\n",
    "            X.append(train_seq)\n",
    "            y.append(data[in_end:out_end, 0])\n",
    "        in_start += 1\n",
    "\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "\n",
    "def cnn_lstm_model(train, sw_width, in_start=0, verbose_set=0, epochs_num=20, batch_size_set=4):\n",
    "    '''\n",
    "    该函数定义 Encoder-Decoder LSTM 模型\n",
    "    '''\n",
    "    train_x, train_y = sliding_window(train, sw_width, in_start=0)\n",
    "\n",
    "    n_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]\n",
    "\n",
    "    train_y = train_y.reshape((train_y.shape[0], train_y.shape[1], 1))\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu',\n",
    "                                     input_shape=(n_timesteps, n_features)))\n",
    "    model.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(RepeatVector(n_outputs))\n",
    "    model.add(LSTM(200, activation='relu', return_sequences=True))\n",
    "    model.add(TimeDistributed(Dense(100, activation='relu')))\n",
    "    model.add(TimeDistributed(Dense(1)))\n",
    "\n",
    "    model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])\n",
    "    print(model.summary())\n",
    "\n",
    "    model.fit(train_x, train_y,\n",
    "              epochs=epochs_num, batch_size=batch_size_set, verbose=verbose_set)\n",
    "    return model\n",
    "\n",
    "\n",
    "def forecast(model, pred_seq, sw_width):\n",
    "    '''\n",
    "    该函数实现对输入数据的预测\n",
    "    '''\n",
    "    data = np.array(pred_seq)\n",
    "    data = data.reshape((data.shape[0] * data.shape[1], data.shape[2]))\n",
    "\n",
    "    input_x = data[-sw_width:, 0]  # 获取输入数据的最后一周的数据\n",
    "    input_x = input_x.reshape((1, len(input_x), 1))  # 重塑形状[1, sw_width, 1]\n",
    "\n",
    "    yhat = model.predict(input_x, verbose=0)  # 预测下周数据\n",
    "    yhat = yhat[0]  # 获取预测向量\n",
    "    return yhat\n",
    "\n",
    "\n",
    "def evaluate_model(model, train, test, sd_width):\n",
    "    '''\n",
    "    该函数实现模型评估\n",
    "    '''\n",
    "    history_fore = [x for x in train]\n",
    "    predictions = list()  # 用于保存每周的前向验证结果；\n",
    "    for i in range(len(test)):\n",
    "        yhat_sequence = forecast(model, history_fore, sd_width)  # 预测下周的数据\n",
    "        predictions.append(yhat_sequence)  # 保存预测结果\n",
    "        history_fore.append(test[i, :])  # 得到真实的观察结果并添加到历史中以预测下周\n",
    "    print(predictions)\n",
    "    predictions = np.array(predictions)  # 评估一周中每天的预测结果\n",
    "    score, scores = evaluate_forecasts(test[:, :, 0], predictions)\n",
    "\n",
    "    return score, scores, predictions\n",
    "\n",
    "\n",
    "def model_plot(score, scores, days, name):\n",
    "    '''\n",
    "    该函数实现绘制RMSE曲线图\n",
    "    '''\n",
    "    plt.figure(figsize=(8, 6), dpi=150)\n",
    "    plt.plot(days, scores, marker='o', label=name)\n",
    "    plt.grid(linestyle='--', alpha=0.5)\n",
    "    plt.ylabel(r'$RMSE$', size=15)\n",
    "    plt.title('CNN-LSTM 模型预测结果', size=18)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def predictions_plot(predictions, days):\n",
    "    week = list(range(46))\n",
    "    pre_sun = list()\n",
    "    pre_mon = list()\n",
    "    pre_tue = list()\n",
    "    pre_wed = list()\n",
    "    pre_thr = list()\n",
    "    pre_fri = list()\n",
    "    pre_sat = list()\n",
    "    for i in range(len(predictions)):\n",
    "        if i % 7 == 0:\n",
    "            pre_sun.append(predictions[i])\n",
    "        elif i % 7 == 1:\n",
    "            pre_mon.append(predictions[i])\n",
    "        elif i % 7 == 2:\n",
    "            pre_tue.append(predictions[i])\n",
    "        elif i % 7 == 3:\n",
    "            pre_wed.append(predictions[i])\n",
    "        elif i % 7 == 4:\n",
    "            pre_thr.append(predictions[i])\n",
    "        elif i % 7 == 5:\n",
    "            pre_fri.append(predictions[i])\n",
    "        elif i % 7 == 6:\n",
    "            pre_sat.append(predictions[i])\n",
    "    \n",
    "    ax1 = plt.subplot(3, 3, 1)\n",
    "    plt.title(\"SUN\")\n",
    "    plt.xlabel(\"week\")\n",
    "    plt.ylabel(\"global_active_power\")\n",
    "    plt.plot(week, pre_sun, \"oc:\")\n",
    "    ax2 = plt.subplot(3, 3, 2)\n",
    "    plt.title(\"MON\")\n",
    "    plt.xlabel(\"week\")\n",
    "    plt.ylabel(\"global_active_power\")\n",
    "    plt.plot(week, pre_mon, \"oc:\")\n",
    "    ax3 = plt.subplot(3, 3, 3)\n",
    "    plt.title(\"TUE\")\n",
    "    plt.xlabel(\"week\")\n",
    "    plt.ylabel(\"global_active_power\")\n",
    "    plt.plot(week, pre_tue, \"oc:\")\n",
    "    ax4 = plt.subplot(3, 3, 4)\n",
    "    plt.title(\"WED\")\n",
    "    plt.xlabel(\"week\")\n",
    "    plt.ylabel(\"global_active_power\")\n",
    "    plt.plot(week, pre_wed, \"oc:\")\n",
    "    ax5 = plt.subplot(3, 3, 5)\n",
    "    plt.title(\"THR\")\n",
    "    plt.xlabel(\"week\")\n",
    "    plt.ylabel(\"global_active_power\")\n",
    "    plt.plot(week, pre_thr, \"oc:\")\n",
    "    ax6 = plt.subplot(3, 3, 6)\n",
    "    plt.title(\"FRI\")\n",
    "    plt.xlabel(\"week\")\n",
    "    plt.ylabel(\"global_active_power\")\n",
    "    plt.plot(week, pre_fri, \"oc:\")\n",
    "    ax7 = plt.subplot(3, 3, 7)\n",
    "    plt.title(\"SAT\")\n",
    "    plt.xlabel(\"week\")\n",
    "    plt.ylabel(\"global_active_power\")\n",
    "    plt.plot(week, pre_sat, \"oc:\")\n",
    "\n",
    "    plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def main_run(dataset, sw_width, days, name, in_start, verbose, epochs, batch_size):\n",
    "    '''\n",
    "    主函数：数据处理、模型训练流程\n",
    "    '''\n",
    "    # 划分训练集和测试集\n",
    "    train, test = split_dataset(dataset.values)\n",
    "    # 训练模型\n",
    "    model = cnn_lstm_model(train, sw_width, in_start, verbose_set=0, epochs_num=20, batch_size_set=4)\n",
    "    # 计算RMSE\n",
    "    score, scores, predictions = evaluate_model(model, train, test, sw_width)\n",
    "    # 打印分数\n",
    "    summarize_scores(name, score, scores)\n",
    "\n",
    "\n",
    "\n",
    "    # 绘图\n",
    "    predictions = list(np.array(predictions).flatten())\n",
    "    predictions_plot(predictions, days)\n",
    "    model_plot(score, scores, days, name)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "60c978b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_2 (Conv1D)            (None, 12, 64)            256       \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 10, 64)            12352     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 5, 64)             0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 320)               0         \n",
      "_________________________________________________________________\n",
      "repeat_vector_1 (RepeatVecto (None, 7, 320)            0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 7, 200)            416800    \n",
      "_________________________________________________________________\n",
      "time_distributed_2 (TimeDist (None, 7, 100)            20100     \n",
      "_________________________________________________________________\n",
      "time_distributed_3 (TimeDist (None, 7, 1)              101       \n",
      "=================================================================\n",
      "Total params: 449,609\n",
      "Trainable params: 449,609\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "[array([[1641.0809],\n",
      "       [1703.6232],\n",
      "       [1696.5306],\n",
      "       [1696.2411],\n",
      "       [1694.8121],\n",
      "       [1712.2899],\n",
      "       [1732.2911]], dtype=float32), array([[2121.5   ],\n",
      "       [2133.9114],\n",
      "       [2124.0286],\n",
      "       [2114.785 ],\n",
      "       [2106.8662],\n",
      "       [2099.5288],\n",
      "       [2092.3257]], dtype=float32), array([[2017.2555],\n",
      "       [2043.8818],\n",
      "       [2029.2794],\n",
      "       [2027.6622],\n",
      "       [2032.0334],\n",
      "       [2051.3503],\n",
      "       [2075.9993]], dtype=float32), array([[2116.6208],\n",
      "       [2153.2346],\n",
      "       [2134.4436],\n",
      "       [2130.4097],\n",
      "       [2129.8682],\n",
      "       [2129.754 ],\n",
      "       [2143.659 ]], dtype=float32), array([[2406.5032],\n",
      "       [2422.5828],\n",
      "       [2399.4104],\n",
      "       [2386.5007],\n",
      "       [2384.824 ],\n",
      "       [2384.378 ],\n",
      "       [2389.2354]], dtype=float32), array([[2288.8154],\n",
      "       [2328.1658],\n",
      "       [2312.7952],\n",
      "       [2302.4087],\n",
      "       [2296.596 ],\n",
      "       [2293.867 ],\n",
      "       [2293.6047]], dtype=float32), array([[2041.987 ],\n",
      "       [2101.498 ],\n",
      "       [2084.696 ],\n",
      "       [2082.988 ],\n",
      "       [2082.595 ],\n",
      "       [2088.5437],\n",
      "       [2108.0745]], dtype=float32), array([[1972.1844],\n",
      "       [1992.9047],\n",
      "       [1971.7084],\n",
      "       [1968.808 ],\n",
      "       [1966.401 ],\n",
      "       [1964.7013],\n",
      "       [1977.7921]], dtype=float32), array([[1963.257 ],\n",
      "       [1978.9552],\n",
      "       [1960.6909],\n",
      "       [1955.3685],\n",
      "       [1953.2631],\n",
      "       [1952.9716],\n",
      "       [1961.5103]], dtype=float32), array([[1435.4796],\n",
      "       [1478.2771],\n",
      "       [1481.7863],\n",
      "       [1487.4768],\n",
      "       [1503.6229],\n",
      "       [1519.0455],\n",
      "       [1537.1439]], dtype=float32), array([[1710.7462],\n",
      "       [1712.3724],\n",
      "       [1706.8031],\n",
      "       [1702.2974],\n",
      "       [1698.091 ],\n",
      "       [1694.0753],\n",
      "       [1690.4445]], dtype=float32), array([[1823.0052],\n",
      "       [1850.5837],\n",
      "       [1836.0009],\n",
      "       [1832.3265],\n",
      "       [1831.9528],\n",
      "       [1833.4437],\n",
      "       [1847.6666]], dtype=float32), array([[1789.6437],\n",
      "       [1807.5765],\n",
      "       [1789.5868],\n",
      "       [1784.9448],\n",
      "       [1782.226 ],\n",
      "       [1781.2954],\n",
      "       [1784.7202]], dtype=float32), array([[1798.3156],\n",
      "       [1819.6696],\n",
      "       [1803.8798],\n",
      "       [1800.3031],\n",
      "       [1798.5239],\n",
      "       [1796.4304],\n",
      "       [1806.966 ]], dtype=float32), array([[1545.4261],\n",
      "       [1582.2487],\n",
      "       [1569.799 ],\n",
      "       [1569.1201],\n",
      "       [1568.8624],\n",
      "       [1584.0885],\n",
      "       [1601.0048]], dtype=float32), array([[1671.0752],\n",
      "       [1688.8724],\n",
      "       [1676.664 ],\n",
      "       [1666.7893],\n",
      "       [1660.3932],\n",
      "       [1657.1517],\n",
      "       [1655.9672]], dtype=float32), array([[1504.7968],\n",
      "       [1535.7196],\n",
      "       [1523.9794],\n",
      "       [1520.3395],\n",
      "       [1519.393 ],\n",
      "       [1517.2006],\n",
      "       [1528.5133]], dtype=float32), array([[1359.6237],\n",
      "       [1373.8427],\n",
      "       [1365.168 ],\n",
      "       [1363.4867],\n",
      "       [1365.6278],\n",
      "       [1379.0599],\n",
      "       [1399.746 ]], dtype=float32), array([[1436.6245],\n",
      "       [1442.6077],\n",
      "       [1429.932 ],\n",
      "       [1419.9913],\n",
      "       [1415.3937],\n",
      "       [1413.8988],\n",
      "       [1411.1469]], dtype=float32), array([[1757.1256],\n",
      "       [1766.5436],\n",
      "       [1750.1237],\n",
      "       [1739.3761],\n",
      "       [1737.6757],\n",
      "       [1736.7992],\n",
      "       [1737.9235]], dtype=float32), array([[1614.9735],\n",
      "       [1645.4615],\n",
      "       [1636.9277],\n",
      "       [1638.2555],\n",
      "       [1658.6442],\n",
      "       [1684.7638],\n",
      "       [1729.1947]], dtype=float32), array([[1542.952 ],\n",
      "       [1548.0109],\n",
      "       [1531.2302],\n",
      "       [1524.6893],\n",
      "       [1524.0018],\n",
      "       [1524.413 ],\n",
      "       [1533.6385]], dtype=float32), array([[1381.203 ],\n",
      "       [1402.5162],\n",
      "       [1395.8806],\n",
      "       [1402.2184],\n",
      "       [1421.6844],\n",
      "       [1452.8387],\n",
      "       [1497.2823]], dtype=float32), array([[1491.413 ],\n",
      "       [1508.0707],\n",
      "       [1499.6211],\n",
      "       [1500.8704],\n",
      "       [1518.5839],\n",
      "       [1538.4187],\n",
      "       [1570.3451]], dtype=float32), array([[1391.2748],\n",
      "       [1414.623 ],\n",
      "       [1406.6615],\n",
      "       [1406.8129],\n",
      "       [1417.2623],\n",
      "       [1434.8422],\n",
      "       [1460.2533]], dtype=float32), array([[1330.2542],\n",
      "       [1346.7672],\n",
      "       [1336.4572],\n",
      "       [1333.7657],\n",
      "       [1329.9344],\n",
      "       [1337.8344],\n",
      "       [1353.336 ]], dtype=float32), array([[1385.7225],\n",
      "       [1396.7137],\n",
      "       [1386.7694],\n",
      "       [1379.7755],\n",
      "       [1373.58  ],\n",
      "       [1367.4498],\n",
      "       [1362.1107]], dtype=float32), array([[1216.4979],\n",
      "       [1227.7152],\n",
      "       [1218.1056],\n",
      "       [1215.9995],\n",
      "       [1214.988 ],\n",
      "       [1221.5085],\n",
      "       [1233.5343]], dtype=float32), array([[1075.9625],\n",
      "       [1094.5297],\n",
      "       [1087.1742],\n",
      "       [1083.7743],\n",
      "       [1081.7837],\n",
      "       [1079.5695],\n",
      "       [1079.5028]], dtype=float32), array([[1170.6044],\n",
      "       [1181.571 ],\n",
      "       [1175.3436],\n",
      "       [1170.5349],\n",
      "       [1166.0714],\n",
      "       [1161.6835],\n",
      "       [1158.0493]], dtype=float32), array([[827.46356],\n",
      "       [854.28394],\n",
      "       [850.732  ],\n",
      "       [857.1385 ],\n",
      "       [870.6977 ],\n",
      "       [894.43335],\n",
      "       [923.23285]], dtype=float32), array([[693.81195],\n",
      "       [695.89795],\n",
      "       [690.9439 ],\n",
      "       [686.8999 ],\n",
      "       [683.59894],\n",
      "       [680.33014],\n",
      "       [677.46545]], dtype=float32), array([[554.93097],\n",
      "       [560.2184 ],\n",
      "       [554.5304 ],\n",
      "       [553.0202 ],\n",
      "       [552.0416 ],\n",
      "       [551.6976 ],\n",
      "       [553.06256]], dtype=float32), array([[1053.2377],\n",
      "       [1050.1774],\n",
      "       [1042.5513],\n",
      "       [1037.2313],\n",
      "       [1032.2633],\n",
      "       [1027.4279],\n",
      "       [1024.3002]], dtype=float32), array([[1316.2339],\n",
      "       [1320.9032],\n",
      "       [1310.7628],\n",
      "       [1321.3927],\n",
      "       [1339.4471],\n",
      "       [1362.4786],\n",
      "       [1389.4847]], dtype=float32), array([[1479.987 ],\n",
      "       [1491.4492],\n",
      "       [1484.5477],\n",
      "       [1489.7673],\n",
      "       [1511.3062],\n",
      "       [1532.1112],\n",
      "       [1566.8372]], dtype=float32), array([[1422.5497],\n",
      "       [1441.5729],\n",
      "       [1430.3478],\n",
      "       [1427.4716],\n",
      "       [1426.6273],\n",
      "       [1427.8732],\n",
      "       [1439.0269]], dtype=float32), array([[1360.2003],\n",
      "       [1377.2479],\n",
      "       [1370.1676],\n",
      "       [1372.044 ],\n",
      "       [1386.8376],\n",
      "       [1407.8195],\n",
      "       [1443.3733]], dtype=float32), array([[1357.5826],\n",
      "       [1378.5508],\n",
      "       [1370.5343],\n",
      "       [1365.3375],\n",
      "       [1361.6929],\n",
      "       [1361.8164],\n",
      "       [1361.6696]], dtype=float32), array([[1488.3561],\n",
      "       [1501.1276],\n",
      "       [1488.9899],\n",
      "       [1486.6215],\n",
      "       [1486.3181],\n",
      "       [1492.7006],\n",
      "       [1505.8674]], dtype=float32), array([[1514.8546],\n",
      "       [1539.6028],\n",
      "       [1530.1913],\n",
      "       [1528.1696],\n",
      "       [1534.1263],\n",
      "       [1553.2692],\n",
      "       [1579.5353]], dtype=float32), array([[1613.2684],\n",
      "       [1639.6608],\n",
      "       [1627.572 ],\n",
      "       [1621.6562],\n",
      "       [1617.5482],\n",
      "       [1615.2076],\n",
      "       [1612.0466]], dtype=float32), array([[1757.2952],\n",
      "       [1791.0248],\n",
      "       [1783.8958],\n",
      "       [1779.9141],\n",
      "       [1776.3746],\n",
      "       [1772.8093],\n",
      "       [1768.296 ]], dtype=float32), array([[1623.9122],\n",
      "       [1635.5154],\n",
      "       [1625.4171],\n",
      "       [1625.0126],\n",
      "       [1636.1544],\n",
      "       [1657.1056],\n",
      "       [1692.8258]], dtype=float32), array([[1576.9741],\n",
      "       [1586.5092],\n",
      "       [1575.3646],\n",
      "       [1570.6278],\n",
      "       [1570.3392],\n",
      "       [1575.2115],\n",
      "       [1588.8146]], dtype=float32), array([[1683.6107],\n",
      "       [1720.097 ],\n",
      "       [1706.8496],\n",
      "       [1704.6168],\n",
      "       [1704.5697],\n",
      "       [1712.2177],\n",
      "       [1728.8628]], dtype=float32)]\n",
      "actual.shape[0]:46, actual.shape[1]:7\n",
      "CNN-LSTM: [377.045] 383.0, 391.3, 340.8, 364.5, 388.2, 335.9, 427.5\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = pd.read_csv('household_power_consumption_days.csv', header=0,\n",
    "                        infer_datetime_format=True, engine='c',\n",
    "                        parse_dates=['datetime'], index_col=['datetime'])\n",
    "\n",
    "days = ['sun', 'mon', 'tue', 'wed', 'thr', 'fri', 'sat']\n",
    "name = 'CNN-LSTM'\n",
    "sliding_window_width = 14\n",
    "input_sequence_start = 0\n",
    "epochs_num = 20\n",
    "batch_size_set = 16\n",
    "verbose_set = 0\n",
    "main_run(dataset, sliding_window_width, days, name, input_sequence_start,\n",
    "             verbose_set, epochs_num, batch_size_set)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "865d8b2eb28e274047ba64063dfb6a2aabf0dfec4905d304d7a76618dae6fdd4"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
